Yawing based IoT Monitoring System to Improve Horizontal Axis Wind Turbine Performance - Alif Ilham Virdaus; Ivan Abdhira Sukriyandoko; Aldi Fahli Muzaqih; Alfido Marchandi Faizatama; Feri Adriyanto (Universitas Sebelas Maret (UNS), 2023)
The diminishing availability of non-renewable energy resources such as coal, oil, and natural gas has prompted efforts to seek sustainable energy alternatives. One promising alternative is wind energy, which can be converted into electricity through wind turbines. However, Horizontal Axis Wind Turbines (HAWTs) have limitations in capturing wind from various directions, affecting operational efficiency. Therefore, this research attempts to address this issue through an innovation in yawing-based monitoring systems integrated with HAWTs and Internet of Things (IoT) technology. The yawing-based monitoring system is designed to monitor the performance of HAWTs in real time, including wind speed, rotations per minute (rpm), electrical current, and voltage. Data obtained from this monitoring system is used to identify potential damage to HAWTs, enabling timely preventive measures. Furthermore, this monitoring system can enhance the operational efficiency of HAWTs, reduce maintenance costs, and extend their lifespan. The results obtained from the comparison between the conventional system and the system with active yawing show a significant increase in power generated by the turbines equipped with the active yawing system. On average, turbines with the conventional system produce 213 watts of power, while turbines equipped with the active yawing system reach a power output of 296 watts. This represents a 39% increase in turbine efficiency, enhancing wind energy capture efficiency. These findings confirm that the integration of the active yawing system can optimally align the turbines with the incoming wind direction, thereby improving the overall system performance.